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‎examples/dynamo/llama_benchmark.py

Lines changed: 0 additions & 77 deletions
This file was deleted.

‎examples/dynamo/llm/run_llm.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -41,7 +41,7 @@ def get_model(args):
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args.model,
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use_cache=False,
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attn_implementation="sdpa",
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# num_hidden_layers=1
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num_hidden_layers=2
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)
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.eval()
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.cuda()
@@ -59,7 +59,7 @@ def get_model(args):
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def compile_torchtrt(model, input_ids, args):
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max_seq_len = input_ids.shape[1] + args.num_tokens
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ep = export_llm(model, input_ids, max_seq_len=max_seq_len)
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position_ids = torch.arange(input_ids.shape[1]).unsqueeze(0).to(DEVICE)
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# Set precision specific flags
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use_fp32_acc = False
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use_explicit_typing = False
@@ -76,7 +76,7 @@ def compile_torchtrt(model, input_ids, args):
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with (torch_tensorrt.logging.debug() if args.debug else nullcontext()):
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trt_model = torch_tensorrt.dynamo.compile(
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ep,
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inputs=[input_ids],
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inputs=[input_ids, position_ids],
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enabled_precisions=enabled_precisions,
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# truncate_double=True,
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use_explicit_typing=use_explicit_typing,

‎examples/dynamo/llm/test_gemma.py

Lines changed: 258 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,258 @@
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import torch
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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6+
import torch.nn as nn
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from torch.testing._internal.common_utils import run_tests
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from torch.testing._internal.common_utils import TestCase
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from transformers.models.gemma3.modeling_gemma3 import Gemma3Attention, Gemma3DecoderLayer
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from transformers.models.gemma3.configuration_gemma3 import Gemma3Config
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from transformers import AutoModelForCausalLM
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import torch_tensorrt
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from contextlib import nullcontext
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import argparse
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import sys
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import os
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# Register SDPA as a standalone operator. Converter and lowering pass are defined in register_sdpa.py
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from register_sdpa import *
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ATOL = 1e-5
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RTOL = 1e-5
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gemma3_model_name = "google/gemma-3-1b-it"
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gemma3_model = AutoModelForCausalLM.from_pretrained(
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gemma3_model_name,
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use_cache=False,
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attn_implementation="sdpa",
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num_hidden_layers=1,
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).eval().cuda()
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GEMMA3_CONFIG = gemma3_model.config
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def print_diff(tensor1, tensor2, prefix=""):
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"""
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Print the diff between two tensors
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"""
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print(f"[{prefix}] Diff between tensor1 and tensor2: {torch.mean(torch.abs(tensor1 - tensor2))}")
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def test_gemma3_attention(args):
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DTYPE = torch.float32
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if args.precision == "FP16":
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DTYPE = torch.float16
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elif args.precision == "BF16":
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DTYPE = torch.bfloat16
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# Set precision specific flags
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use_fp32_acc = False
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use_explicit_typing = False
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if args.precision == "FP16":
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enabled_precisions = {torch.float32}
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use_fp32_acc = True
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use_explicit_typing = True
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elif args.precision == "BF16":
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enabled_precisions = {torch.bfloat16}
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use_fp32_acc = False
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else:
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enabled_precisions = {torch.float32}
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model = gemma3_model.model.layers[0].self_attn.to(DTYPE)
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# gemma3
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hidden_states = torch.randn((1, 5, 1152), dtype=DTYPE).cuda()
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position_embeddings = (torch.randn((1, 5, 256), dtype=DTYPE).cuda(), torch.randn((1, 5, 256), dtype=DTYPE).cuda())
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pyt_output = model(hidden_states, position_embeddings, None)
71+
seq_len = torch.export.Dim("seq_len", min=2, max=2176)
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dynamic_shapes = ({1: seq_len}, ({1: seq_len}, {1: seq_len}), None)
73+
ep = torch.export.export(model, (hidden_states, position_embeddings, None), dynamic_shapes=dynamic_shapes)
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with (torch_tensorrt.logging.debug() if args.debug else nullcontext()):
76+
trt_model = torch_tensorrt.dynamo.compile(ep,
77+
inputs=[hidden_states, position_embeddings, None],
78+
enabled_precisions=enabled_precisions,
79+
disable_tf32=True,
80+
use_fp32_acc=use_fp32_acc,
81+
use_explicit_typing=use_explicit_typing,
82+
debug=args.debug)
83+
trt_output = trt_model(hidden_states, position_embeddings, None)
84+
85+
if isinstance(pyt_output, tuple):
86+
print_diff(pyt_output[0], trt_output[0], "Diff b/w pyt and trt")
87+
assert torch.allclose(pyt_output[0], trt_output[0], atol=ATOL, rtol=RTOL)
88+
else:
89+
print_diff(pyt_output, trt_output, "Diff b/w pyt and trt")
90+
assert torch.allclose(pyt_output, trt_output, atol=ATOL, rtol=RTOL)
91+
92+
def test_gemma3_attention_with_static_cache(args):
93+
94+
import static_cache_v2
95+
DTYPE = torch.float32
96+
model = gemma3_model.model.layers[0].self_attn.to(DTYPE)
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# Inputs
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ISL = 2048
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NUM_TOKENS = 128
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OSL = ISL + NUM_TOKENS
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hidden_states = torch.randn((1, ISL, 1152), dtype=DTYPE).cuda()
103+
position_embeddings = (torch.randn((1, ISL, 256), dtype=DTYPE).cuda(), torch.randn((1, ISL, 256), dtype=DTYPE).cuda())
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key_cache = torch.zeros(1, 4, OSL, 64).cuda().to(DTYPE)
105+
value_cache = torch.zeros(1, 4, OSL, 64).cuda().to(DTYPE)
106+
start_idx = 0
107+
end_idx = ISL
108+
is_causal = True
109+
110+
pyt_output = model(hidden_states, position_embeddings, None)
111+
seq_len = torch.export.Dim("seq_len", min=2, max=2176)
112+
dynamic_shapes = ({1: seq_len}, ({1: seq_len}, {1: seq_len}), None)
113+
ep = torch.export.export(model, (hidden_states, position_embeddings, None), dynamic_shapes=dynamic_shapes)
114+
with (torch_tensorrt.logging.debug() if args.debug else nullcontext()):
115+
trt_model = torch_tensorrt.dynamo.compile(ep,
116+
inputs=[hidden_states, position_embeddings, None, key_cache, value_cache, start_idx, end_idx, is_causal],
117+
enabled_precisions={torch.float32},
118+
disable_tf32=True,
119+
debug=args.debug,
120+
# offload_module_to_cpu=True,
121+
use_python_runtime=True)
122+
123+
# Test Prefill
124+
trt_output, _, key_cache, value_cache = trt_model(hidden_states, position_embeddings, None, key_cache, value_cache, start_idx, end_idx, is_causal)
125+
print_diff(pyt_output[0], trt_output[0], "pyt_output[0] vs trt_output[0] [Prefill]")
126+
127+
# Test Generate
128+
for start_idx in range(2048, 2176):
129+
end_idx = start_idx + 1
130+
hidden_states_curr = torch.randn((1, 1, 1152), dtype=DTYPE).cuda()
131+
position_embeddings_curr = (torch.randn((1, 1, 256), dtype=DTYPE).cuda(), torch.randn((1, 1, 256), dtype=DTYPE).cuda())
132+
# Concatenate the current hidden_states with the previous ones
133+
hidden_states_full = torch.cat((hidden_states, hidden_states_curr), dim=1)
134+
position_embeddings_full = (torch.cat((position_embeddings[0], position_embeddings_curr[0]), dim=1), torch.cat((position_embeddings[1], position_embeddings_curr[1]), dim=1))
135+
136+
is_causal = False
137+
out_no_cache, _ = model(hidden_states_full, position_embeddings_full, None)
138+
out_trt, _, key_cache, value_cache = trt_model(hidden_states_curr, position_embeddings_curr, None, key_cache, value_cache, start_idx, end_idx, is_causal)
139+
out_pyt = out_no_cache[:, -1:, :]
140+
print_diff(out_pyt, out_trt, f"pyt_curr_output vs out_trt for idx {start_idx}")
141+
142+
hidden_states = hidden_states_full
143+
position_embeddings = position_embeddings_full
144+
145+
def test_gemma3_decoder(args):
146+
147+
DTYPE = torch.float32
148+
if args.precision == "FP16":
149+
DTYPE = torch.float16
150+
elif args.precision == "BF16":
151+
DTYPE = torch.bfloat16
152+
model = gemma3_model.model.layers[0].to(DTYPE)
153+
# model.self_attn.is_sliding = False
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# gemma3
156+
hidden_states = torch.randn((1, 6, 1152), dtype=DTYPE).cuda()
157+
position_embeddings_global = (torch.randn((1, 6, 256), dtype=DTYPE).cuda(), torch.randn((1, 6, 256), dtype=DTYPE).cuda())
158+
position_embeddings_local = (torch.randn((1, 6, 256), dtype=DTYPE).cuda(), torch.randn((1, 6, 256), dtype=DTYPE).cuda())
159+
160+
pyt_output = model(hidden_states, position_embeddings_global, position_embeddings_local)
161+
seq_len = torch.export.Dim("seq_len", min=2, max=2176)
162+
dynamic_shapes = ({1: seq_len}, ({1: seq_len}, {1: seq_len}), ({1: seq_len}, {1: seq_len}))
163+
ep = torch.export.export(model, (hidden_states, position_embeddings_global, position_embeddings_local), dynamic_shapes=dynamic_shapes)
164+
165+
with (torch_tensorrt.logging.debug() if args.debug else nullcontext()):
166+
trt_model = torch_tensorrt.dynamo.compile(ep,
167+
inputs=[hidden_states, position_embeddings_global, position_embeddings_local],
168+
enabled_precisions={torch.float32},
169+
debug=args.debug)
170+
trt_output = trt_model(hidden_states, position_embeddings_global, position_embeddings_local)
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print(f"Diff b/w pyt and trt: {torch.mean(torch.abs(pyt_output[0] - trt_output[0]))}")
173+
# breakpoint()
174+
assert torch.allclose(pyt_output[0], trt_output[0], atol=ATOL, rtol=RTOL)
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176+
def test_gemma3_decoder_with_static_cache(args):
177+
178+
class Gemma3DecoderLayerBlock(nn.Module):
179+
def __init__(self, model):
180+
super().__init__()
181+
self.config = GEMMA3_CONFIG
182+
self.decoder = Gemma3DecoderLayer(
183+
config=self.config,
184+
layer_idx=0)
185+
self.model = model
186+
def forward(self, hidden_states, position_embeddings):
187+
return self.model(hidden_states, position_embeddings=position_embeddings)
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DTYPE = torch.float32
190+
model = Gemma3DecoderLayerBlock(gemma3_model.model.layers[0].to(DTYPE))
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import static_cache_v2
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# Inputs
194+
ISL = 2048
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NUM_TOKENS = 128
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OSL = ISL + NUM_TOKENS
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hidden_states = torch.randn((1, ISL, 1152), dtype=DTYPE).cuda()
198+
position_embeddings_global = (torch.randn((1, ISL, 256), dtype=DTYPE).cuda(), torch.randn((1, ISL, 256), dtype=DTYPE).cuda())
199+
position_embeddings_local = (torch.randn((1, NUM_TOKENS, 256), dtype=DTYPE).cuda(), torch.randn((1, NUM_TOKENS, 256), dtype=DTYPE).cuda())
200+
key_cache = torch.zeros(1, 4, OSL, 64).cuda().to(DTYPE)
201+
value_cache = torch.zeros(1, 4, OSL, 64).cuda().to(DTYPE)
202+
start_idx = 0
203+
end_idx = ISL
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is_causal = True
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pyt_output = model(hidden_states, position_embeddings_global, position_embeddings_local)
207+
seq_len = torch.export.Dim("seq_len", min=2, max=2176)
208+
dynamic_shapes = ({1: seq_len}, ({1: seq_len}, {1: seq_len}))
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ep = torch.export.export(model, args=(hidden_states, position_embeddings), dynamic_shapes=dynamic_shapes)
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211+
with (torch_tensorrt.logging.debug() if args.debug else nullcontext()):
212+
trt_model = torch_tensorrt.dynamo.compile(ep,
213+
arg_inputs=[hidden_states, position_embeddings, key_cache, value_cache, start_idx, end_idx, is_causal],
214+
enabled_precisions={torch.float32},
215+
disable_tf32=True,
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debug=args.debug,
217+
# offload_module_to_cpu=True,
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use_python_runtime=True)
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# Test Prefill
221+
trt_output, key_cache, value_cache = trt_model(hidden_states, position_embeddings, key_cache, value_cache, start_idx, end_idx, is_causal)
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print_diff(pyt_output[0], trt_output, "pyt_output vs trt_output [Prefill]")
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# Test Generate
225+
for start_idx in range(2048, 2176):
226+
end_idx = start_idx + 1
227+
hidden_states_curr = torch.randn((1, 1, 1152), dtype=DTYPE).cuda()
228+
position_embeddings_curr = (torch.randn((1, 1, 256), dtype=DTYPE).cuda(), torch.randn((1, 1, 256), dtype=DTYPE).cuda())
229+
# Concatenate the current hidden_states with the previous ones
230+
hidden_states_full = torch.cat((hidden_states, hidden_states_curr), dim=1)
231+
position_embeddings_full = (torch.cat((position_embeddings[0], position_embeddings_curr[0]), dim=1), torch.cat((position_embeddings[1], position_embeddings_curr[1]), dim=1))
232+
233+
is_causal = False
234+
out_no_cache = model(hidden_states_full, position_embeddings_full)
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236+
out_trt, key_cache, value_cache = trt_model(hidden_states_curr, position_embeddings_curr, key_cache, value_cache, start_idx, end_idx, is_causal)
237+
out_pyt = out_no_cache[0][:, -1:, :]
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print_diff(out_pyt, out_trt, f"pyt_curr_output vs out_trt for idx {start_idx}")
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hidden_states = hidden_states_full
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position_embeddings = position_embeddings_full
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if __name__ == "__main__":
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arg_parser = argparse.ArgumentParser(
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description="Run test cases for llama attention and decoder"
246+
)
247+
arg_parser.add_argument(
248+
"--debug",
249+
action="store_true",
250+
help="Enable debug (default: False)"
251+
)
252+
arg_parser.add_argument("--precision", type=str, default="FP16", help="Precision to use in the model. Options: FP16, BF16, FP32")
253+
args = arg_parser.parse_args()
254+
with torch.inference_mode():
255+
# test_gemma3_attention(args)
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# test_gemma3_attention_with_static_cache(args)
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test_gemma3_decoder(args)
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# test_gemma3_decoder_with_static_cache(args)

‎examples/dynamo/llm/test_qwen2.5_components.py

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,6 @@
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import torch.nn as nn
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from torch.testing._internal.common_utils import run_tests
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from torch.testing._internal.common_utils import TestCase
9-
from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer
109
from transformers.models.llama.configuration_llama import LlamaConfig
1110
from transformers import AutoModelForCausalLM
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import torch_tensorrt

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